基于LSMM和改进的FCM提取城市植被覆盖度——以北京市海淀区为例

植被是城市生态系统的重要组成部分,及时获取植被覆盖信息对城市生态环境监测具有重要意义。利用中分辨率Landsat TM遥感数据,采用线性光谱分解模型(LSMM)开展城市植被覆盖度提取;同时,通过改进训练样本选择方式,在最小噪声变换(MNF)、像元纯净指数分析(PPI)、N维可视化分析基础上得到端元样本,再运用模糊C-均值(FCM)获取植被覆盖度;最后以高分辨率SPOT5遥感数据对两种方式的提取结果进行精度检验。结果显示,基于LSMM和改进的FCM提取的城市植被覆盖度与检验数据相关系数分别为0.8252和0.9381,后者可以较好地处理其他要素的非线性影响,因而具有较高精度。

Extract urban vegetation coverage based on LSMM and improved FCM: a case study in Haidian District

Vegetation is an important part of urban ecosystem; therefore timely access to vegetation coverage information is of great significance for monitoring urban ecological environment. Linear spectral mixture model (LSMM) was carried out for urban vegetation coverage extraction using medium-resolution Landsat TM remote sensing data. Meanwhile, the fuzzy c-means (FCM) method was chosen to extract vegetation coverage by improving the training sample selection method to obtain the end-member sample based on minimum noise transform (MNF), pixel purity index analysis (PPI), and N-dimensional visualization analysis. Finally, high-resolution SPOT5 remote sensing data extracted in two ways were used to carry out the accuracy test for vegetation coverage. The results showed that the correlation coefficients between the inspection data and LSMM-based and improved FCM-based data were 0.8252 and 0.9381, respectively. It indicated that the improved FCM-based method with higher accuracy can better eliminate the nonlinear effect of other elements.